Increasing the selectivity of chemical sensors, while at the same time reducing their complexity, size, and cost, are challenges to the sensing community. To this end, an area of exploration has been the development of filter-based chemical sensors and related methods.
Optical filter-based approaches to chemical sensing have the potential to develop simple, small, low-cost sensors with high selectivity. Such techniques can be designed in a manner that replicates the mechanism of human color vision, which utilizes three broadly overlapping filters to discriminate between over two million potential color hues. These approaches are used to identify multiple analytes in complex backgrounds in the near-infrared (near-IR), for example, such as glucose/urea and dimethyl methylphosphonate/diisopropyl methylphosphonate.
The present invention provides an approach that utilizes multiple, broadband, infrared (IR) filters to enable the discrimination of target chemicals in the presence of potential interferents that have IR spectral signatures in a limited waveband. This analysis technique, CDSD, utilizes a set of broad-IR transmission filters, to discriminate between a specific target chemical and multiple interferents with strongly overlapping IR spectra.
The CDSD approach has some similarities to previously existing methods, but is different in many respects. For example, CDSD requires that filtered data be used, but both expands the dimensionality of the configuration-space, and employs comparative relationships between the filter responses of the various chemicals in a given set. Other approaches use a filter-defined configuration vector space within which an attempt is made to find projections that separate chemical signatures from their background. These approaches are configuration-space reduction methods, whereas CDSD is a higher-dimensionality space-expansion method. Other filter-based detection approaches include Programmable Correlation Radiometry, a spectral comparative-radiometry technique, and Multivariate Optical Elements, which use multilayer interference filters, whose transmission spectra represent the features of a spectral regression vector within a given spectral region. Programmable Correlation Radiometry uses correlation spectroscopy and synthetic spectra as a basis for the remote detection of chemical species. Contrary to sensing using techniques such as Programmable Correlation Radiometry or Multivariate Optical Elements, CDSD does not attempt to resolve spectral differences based on optical filter selectivity characteristics. On the contrary, CDSD uses low-resolution, large-bandwidth, overlapping spectral filters to construct the chemical representative vectors in order to explore the relationships between the vectors, as well as their commonly constructed surfaces and volumes. Thus, rather than using complex optical elements, the biomimetic CDSD approach relies on individual chemical responses to simple shaped band-pass optical filters. Therefore, the CDSD method provides a new data processing and detection approach for photometric systems.
The numerical method is not an iterative search, such as Principal Component Analysis (PCA), or a type of linear discriminant analysis (LDA). It actually increases the dimensions in configuration-space, where the computations are performed, instead of decreasing them, as both PCA and LDA do.